Senior GenAI Research Scientist Scaling

San Francisco, CA - USA

Monthly Salary: Not Disclosed

Posted on: 13 days ago

Vacancies: 1 Vacancy

Job Summary

At Databricks we are obsessed with enabling data teams to solve the worlds toughest problems from security threat detection to cancer drug development. We do this by building and running the worlds best data and AI platform so our customers can focus on the high value challenges that are central to their own missions.

The Mosaic AI organization enables companies to develop AI models and systems using their own data with technologies ranging from pre-training LLMs from scratch to augmented generation using the latest retrieval techniques. Mosaic AI does so by producing novel science and putting it into production. Mosaic AI is committed to the belief that a companys AI models are just as valuable as any other core IP and that high-quality AI models should be available to all.

Job Description

As a Research Scientist on the Scaling team you will be responsible for keeping up with the latest developments in deep learning and advancing the scientific frontier by creating new techniques that go beyond the state of the art. You will work together on a collaborative team of researchers and engineers with diverse backgrounds and technical training. And most importantly you will love our customers: our goal is to make our customers successful in applying state-of-the-art LLMs and AI systems and we encode our scientific expertise into our products to make that possible.

The Impact you will have

As a Research Scientist on the Mosaic AI Team at Databricks You Will:

Define and lead independent research agendas on foundation model efficiency in model training and reinforcement learning conducting experiments to empirically validate hypotheses and benchmark against state-of-the-art approaches
Drive algorithmic innovations for large-scale neural network training or inference (e.g. novel optimizers low-precision techniques model adaptation methods)
Optimize ML systems for distributed training memory efficiency and compute efficiency through hands-on implementation.

What We Look for

MS/PhD in Computer Science or related field with strong foundations in machine learning and systems
Proven ability to write high-quality efficient code in Python and PyTorch for research implementation and experimentation
Strong preference for candidates with first-author publications at top ML/systems conferences (ICLR ICML NeurIPS MLSys) focused on optimization or efficiency.

Required Experience:

Senior IC